We stand on the cusp of the fourth industrial revolution, which is characterized by the fusion of physical, digital, and biological worlds. In this transformative phase, the successful integration of Artificial Intelligence (AI) by Bangladesh insurance industry will become a major factor influencing its growth and competitiveness. AI-driven transformations can redefine the industry, streamline operations, and deliver superior customer experiences, aligning with the revolution's key pillars.

AI not only introduces high levels of automation to reduce cost and increase efficiency but also enables the creation of new, innovative insurance products that are tailored to individual needs. Furthermore, by leveraging AI's predictive capabilities, insurers can anticipate future risks and trends more accurately, facilitating proactive decision-making and strategic planning. 

Also Read: Revolutionizing Insurance: AI's Impact On Rate Making

The blend of AI into core business operations, coupled with the human element of creativity, empathy, and strategic thinking, can lead the industry towards an adaptive, resilient, and customer-centric future, fulfilling the promise of the fourth industrial revolution.

What is AI? Simply put, it is a technology that enables man-made machines to simulate human intelligence. As a result, they can perform intelligent tasks previously thought to be only possible by humans. The present world is witnessing a strong acceleration in the development of this field, thanks to the easy access to data, increased computing capabilities and changing consumer expectations. AI is evolving rapidly – creating on its way viable opportunities for business growth. In recent years, there has been extensive use of AI technologies for improving organizational performance across all industries, including insurance.

The leading insurance firms in the USA are using AI in all functional areas: customer service, marketing, claim processing and management, underwriting, and fraud detection to name a few. They use AI, for example, to improve customer experience (CX) by reducing customers’ waiting time. Their intelligent support mechanisms process tens of thousands of queries per month. Insurtech legend Lemonade can process a claim within 5 minutes. AI-led innovative technology is now available to provide a life insurance quote by estimating an individual’s age, gender, and body mass index from the person’s selfie photo or video. 

Read More: Micro-Insurance In Bangladesh: Closing The Protection Gap For Low-Income Individuals

Learning, language processing, motion, problem-solving, perception, reasoning, and vision are the major areas where AI is currently focused on achieving human-like intelligence by combining a multitude of algorithms based on statistical learning, machine learning, deep learning, probabilistic methods, and optimization techniques. The bedrock of this astounding success is the availability of data of all sizes, small, medium, and big. These examples simply indicate that the business is going through a transformation, and that the use of AI is bringing about a paradigm shift. Life and Non-life insurers in Bangladesh must grab this incredible opportunity in order to upgrade their services, especially to remove the barriers associated with traditional underwriting and claim management. Early AI adopters, audacious and far-sighted as they may be, are likely to gain significant ground in the near future.

AI-enablement is a continuous journey for advancement. This is why the insurers should start from where they are now, with whatever resources they have, and should have the mindset to constantly improve their capabilities. The cumulative achievement gained from these developments will differentiate the winners in the troop. 

This article highlights a few business functions wherein local insurance companies may use their own technology stack and existing manpower to embark on AI-led innovation. These are as follows:  1) document processing and digitalization; 2) underwriter ratemaking and reserve; 3) social listening and brand management; and 4) prospect identification and optimized marketing.

Bangladesh Insurance Industry

Bangladesh insurance industry consists of two main sectors: life and non-life. There are 81 active insurers that are currently providing services in these areas where the non-life insurance firms are the majority by number (46/81). However, life insurance carriers generate 75% of the industry gross premium despite being the minority (35/81). 

The number of insurance products available for consumers are limited. For example, major life-insurance products include ordinary, micro, takaful, and group whereas key non-life insurance products comprise fire, marine, motors among others. 

The local insurance industry is a billion-dollar market including both life and non-life gross premium. According to Bangladesh Insurance Development and Regulatory Authority (IDRA) the country has less than 20 million in total insurance coverage. This number is significantly lower than 20-30 million middle class population who has the potential to be the major consumers of insurance products. 

Read More: The Imperative Of Good Governance In The Insurance Industry Of Bangladesh

Despite being an industry with decent market size, the insurance business faced a series of setbacks in the last decade resulting in a negative growth rate. The major contributors to this downturn seem to include absence of a long-term business strategy, lack of professionalism, unethical business practices by agents, and poor CX. A 100% decline in insurance penetration in just 10 years is in stark contrast with the country’s exponential economic growth. Lack of proper oversight and control have caused insurtech disruptors to sprout, grow, and eliminate the incumbents.

To encounter such an existential threat, insurers have no choice other than embracing new technologies without further deferment. Lost business opportunities could be successfully regained if the firms build in-house AI capabilities or collaborate with vendors to leverage outside expertise and innovations. 

Areas of AI-based Innovation 

Document Processing & Digitalization

Paper-based business is still the norm in Bangladesh. This time-consuming, error-prone, and expensive manual practice is one of the major factors contributing to occupational inefficiencies as well as poor CX. Document processing, including digital archiving and data extraction, is the most important step towards digitalization, and subsequently, to AI-enablement. For local firms, digitalization should be the area of the highest priority. A combination of advanced technology and a digital data center guarantees a competitive edge to any insurance firm among its peers.  

Pioneering AI techniques, such as Optical Character Recognition (OCR) and Natural Language Processing (NLP), should be combined to succeed in digitalization efforts. These are publicly available technologies, of which organizations could take advantage through some skill development strategy. Skilled resources would be able to digitalize various documents: Government Forms, NID, Vehicle Number Plates, Bank Loan Forms, Payment Receipts, Policy Forms, Claim Documents, Birth and Death Certificates, etc.

Backend activities, such as underwriting, claims processing, office administrative tasks, etc., are the primary candidates where AI-led automation promises manifold upturn in operational efficiency. This results in reduced overhead associated with labor and real estate, and consequently, enables firms to offer products at a competitive price.

Mobile enabled services are important to go beyond digitalization because they reduce reliance on intermediaries, increase business transparency, increase customer convenience by providing easy access to information, and facilitate means to reach out to larger audience. Despite the wide-ranging benefits only 30% insurance firms have mobile apps registered in the Google Play. Moreover, a majority of these apps lack standards to meet customers’ needs as well as expectations. This observation suggest that the incumbents have not yet fully understand the necessity to invest in transformational facilities to satisfy the need of digital consumers.  The prevailing mentality is risky because it makes the local companies vulnerable to insurtech challengers and disruptors.

 Underwriter Ratemaking & Reserve

Insurers routinely use data, analytics, and statistics to understand customers' behavior. However, the traditional tools and methods employed in the industry have very limited capabilities. The existing methods are suitable for shallow (limited number of clients) and lean (a handful of client attributes) datasets but are inadequate for handling deep (millions of customers) and wide (thousands of attributes) datasets. The advanced technology can easily eliminate this roadblock because it can handle customer data of any shape and size. Supported by a wealth of highly sophisticated, publicly available data mining and statistical tools, AI paves the way for the underwriters and insurance market specialists for data driven decision making. 

Actuaries and underwriters use classical tabular models or rate tables to set up the premium structure for customers. For limited amount of data this practice adds value, and its power can be leveraged using conventional technologies. However, for large volume of data this method is not only inadequate because of its inability to scale but also falls short to leverage the full potential of the data.  From the outcomes of many AI-driven use-cases conducted by the insurers in the USA, it is now well established that the introduction of AI algorithms makes significant improvement in actuarial ratemaking models. It also speeds up the entire workflow of the pricing model, increases the productivity of the professionals involved, and supports developing innovative products. 

Identifying Hidden Risk Factors: 

The existing actuarial models typically use a limited number of risk factors, including age, gender, occupation, income, benefit type, and smoking habit, to predict the likelihood of a claim to occur or to predict the likelihood of the duration a claim once it started. This practice precludes customary methods from delivering the best results and make adverse impact on corporate’s claim reserve strategy. The AI offers the capability to develop multitude of such models using hundreds of attributes available in the insurer databases and promises a new standard of underwriting models with improved performance. The AI algorithms can easily recognize new risk factors that would have been missed or overlooked following current best practices.

Examples of AI-based Use-cases:

With support from AI-based algorithms actuaries and underwriters have begun studying to transform various business processes. The insurers in Bangladesh must start exploring areas of ratemaking & reserve to implement AI for automation and scale. By leveraging AI, insurers can make more accurate forecasts and reduce the risk of error, thereby ensuring a more reliable and efficient service. AI-driven automation can also improve operational efficiency, enabling insurers to focus on strategic tasks and customer relationships. Moreover, the capability of AI to process and analyze vast amounts of data can reveal insights that aid in formulating better products, policies, and services for the insured.

1. Actuarial Ratemaking: Optimized pure premium pricing models, e.g., frequency-severity model, can be leveraged in mobile apps to provide customers with robust and customized quotes. 
2. Policy Lapse Model: Classification models can predict the likelihood of an in-force policy to lapse or surrender within 1,2,3 years from the policy start date.   
3. Policy Persistency and Price Elasticity Model: AI models can predict persistency of in-force policies across various product lines. Numerical simulations can test the sensitivity of persistency on premium price change. 
4. Close Ratio Model & the Competitiveness of Rates for New Business.: Regression models can predict success of insurance quote activities and provide valuable insights to the product management and marketing teams. 
5. Claim Incidence & Termination Model: AI models can predict the probability of a new policy to file for a claim or an existing open claim to be terminated within 0.5, 1.0, and 1.5 years.
6. Accelerated Underwriting: Classification models can predict fluidness underwriting path with respect to full underwriting path.
7. Dental & Vision Cost Model: Classification models can predict annual dental and vision claim amounts for individual policy holders.
8. Vehicle Damage Detection Model & Claim Leakage: Predictive models can evaluate vehicle impairment from images and can reduce claim leakage during insurance damage claim processing.   
9. Claims Routing: AI models can be built to automatically route claims documents to the appropriate departments for efficient and expedited processing.
10. Claim Reserving Model: Machine learning models can estimate future claim reserves by considering both claim frequency and claim severity.
11. Experience Rating Models: AI can create models that adapt premiums for individual policyholders based on their specific claim history, improving the accuracy of ratemaking.
12. Dynamic Financial Analysis: Machine learning algorithms can model and predict complex relationships between different risk factors in ratemaking. These models can include various types of risks such as underwriting, credit, market, and operational risks.
13. Predictive Analysis for Reinsurance: AI can be used to analyze the historical data and predict the need for reinsurance contracts to balance the risk portfolio and assist in ratemaking.
14. Catastrophe Modelling: AI can predict the financial impact of catastrophic events like floods or earthquakes on insurance reserves. These models help insurers in estimating the potential losses and maintaining appropriate reserves.
15. Reserve Adequacy Assessment: AI models can analyze claims data to predict future claim amounts and evaluate the adequacy of current reserve levels. This assists in maintaining the financial health of the insurance company.

Social Listening & Brand Management 

Social Listening is a process of understanding online conversation about a company or brand as well as its products and services. This technique allows a firm to track electronic platforms for mentions and conversations, and then to analyze the data to monitor online sentiments. It helps learning about positive and negative user experience, which in turn, provide valuable insight about product-market fit. Companies can employ web scarping technology and NLP-based models to track social sentiments prompted from user-product interactions. 

An AI program can automatically collect information from the web, analyze information by running algorithms, extract novel insights from the data, and providing suggestion for informed decision making. 

In the context of the Bangladesh insurance industry, Social Listening can play a crucial role in understanding customer needs, preferences, and sentiments towards different insurance products and services. With a significant proportion of the population now active on various social media platforms, customers are freely expressing their views and experiences about different insurance companies and their offerings. Analyzing this user-generated content through AI-powered Social Listening tools can provide insurers with direct customer feedback and rich insights for brand management. 

By tracking customer sentiments and understanding their pain points, insurers can strategically improve their offerings, enhance customer service, and design personalized marketing campaigns to strengthen their brand image. In a highly competitive market like Bangladesh, such proactive and customer-centric approaches can significantly enhance a brand's reputation and customer loyalty.

Prospect Identification

Digital platform is an indispensable medium to reach out to the masses. However, local firms conduct product marketing in a very informal manner. Insurance agents typically make use of unreliable and un-scalable methods to find new customers.

Firms can leverage existing customer database and combine with external data to develop ‘customer look-alike model’ for optimized lead/target generation. Robust AI algorithms, including cluster techniques or ML predictive modeling, can be used to augment the identification of target audience across all product lines. The automated target generation process will not only boost agents’ performance in lead generation but also provide cross-sell and up-sell opportunities to the existing customers. 

Best Practices in Acquiring New Customers:

Insurers thrive by acquiring new customers because they ensure a steady revenue stream. Local firms use traditional services, such as brokers and agents, to acquire new customers. The companies could also obtain customers through other channels including web services, mobile services, etc.  To reap the benefit of these communications channels in generating new business leads requires innovative tools and techniques. This requires going beyond the traditional means and this is where AI can play an important role to identify groups of people from target population who are highly likely to become future customers.

Best Practices in Retaining Existing Customers:

Like in any other business, the survival of insurers is closely tied to customer retention. Converting existing customers into a loyal client-base entails understanding consumers’ behavior. Novel AI algorithms can be used to effectively separate existing customers into various groups based on various historical transactions. This segmented view could provide companies a great opportunity to assess their customers, associated policies, their designs, and managements. The segmentation could also be used to promote cross-sell and up-sell opportunities among customers.

Final Words

Thanks to its robust and consistently growing economy, Bangladesh is experiencing a rise in its middle-class population. Given this, the negative growth rate of insurance industry indicates a collective failure in widening its customer base. To get out of this unwelcoming situation insurers must take active measures to reach out to the masses with improved products and services. They must also establish transparency and guarantee the best CX.

The widespread success of AI-enabled technologies all over the world shows that product and market diversification though AI adaptation is the way to go for the incumbents. It will accelerate the journey to regain the lost paradise and help re-creating a promising market. Failure to embrace AI technology early would be a missed opportunity rendering the insurers vulnerable to the disruptions.

Within the Bangladesh insurance industry, there exists an unfortunate misunderstanding when it comes to embracing AI-based services. There are two key misconceptions that need addressing.

The first misconception is the belief that the sector is ill-prepared to deliver AI-based services. This notion is largely born from a limited understanding of AI-related processes. It is vital to recognize that the primary component for AI technologies is data, an asset that is abundant for any company. Insurers can begin their AI journey by harnessing this data without the need for immediate immersion into Big Data, thereby side-stepping a significant technology infrastructure upgrade. Existing tools like standard databases in local servers and workstations can be employed effectively to build AI solutions. A considerable challenge might be securing skilled personnel, such as Data Scientists or Machine Learning Engineers, to spearhead these initiatives. This skills gap can be overcome either by re-skilling and up-skilling existing employees or by utilizing resources available in the local market.

The second misconception conflates automation with intelligence, viewing them as the same thing. While these elements are complementary, they are fundamentally distinct. The implementation of systems like Enterprise Resource Planning (ERP) is a form of automation, which streamlines operations and improves efficiency. On the other hand, intelligence through AI involves the ability to learn, adapt, and make informed decisions based on data analysis. Both automation and intelligence have their roles in modernizing operations and enhancing service delivery. However, it is essential to understand their unique contributions and employ them judiciously to achieve the optimal blend of efficiency and innovation.

It's vital to remember that the journey of AI-enablement is a continuous process of learning and adapting. It requires a fundamental shift in mindset and culture towards innovation and agility. Given the rapid pace of technological advancement, insurers in Bangladesh have the opportunity to align themselves with the fourth industrial revolution, leveraging AI to transform business operations and deliver superior customer experiences. As the industry moves forward, insurers that fully embrace AI will stand at the vanguard of innovation, reaping the benefits of improved efficiency, reduced costs, and enhanced customer engagement. By harnessing the power of AI, Bangladesh's insurance industry can carve out a promising, tech-forward future in the rapidly evolving global insurance landscape in the backdrop of fourth industrial revolution.